1.0 Overview

In this take-home exercise, we aim to apply the appropriate data visualisation techniques to create a data visualisation showing the average rating and proportion cocoa percent (% chocolate) great than or equal to 70% by top 15 company location through the use of ggplot2 methods.

2.0 Data Import

The chocolate.csv was used to show the average rating and proportion of cocoa percent (% chocolate) greater or equal to 70% by top 15 company location.

The code chunk below was used to import the necessary packages to create the visualisation.

packages = c('ggstatsplot', 'ggside', 'knitr',
             'tidyverse', 'broom', 'ggdist', 'dplyr','plotly','DT','crosstalk')
for (p in packages){
  if(!require(p, character.only = T)){
    install.packages(p)
  }
}

3.0 Data Preparation

Step 1: Isolate columns needed (i.e. company_location, rating and cocoa_percent) Step 2: Remove “%” from cocoa_percent and convert to numeric.

choco <- read_csv("data/chocolate.csv")

choco$cocoa_percent <- gsub(pattern = "%", replacement = "", x = choco$cocoa_percent) %>% as.numeric(choco$cocoa_percent)

##subsetting the isolated columns

chocodf <- choco %>% select(company_location, rating, cocoa_percent)

##convert rating to numeric

chocodf$rating <- as.numeric(chocodf$rating)

3.1 Average Rating

Creating avg_rating through grouping of data by company location, summarizing the data to get the frequency count, mean and standard deviation
Passing through the output using “%>%” and use of “mutate” to create a new variable standard error (SE = standard deviation / sqrt(n - 1))
Order the final dataset by top 15 company frequencies

avg_rating <- chocodf %>%
  group_by(company_location) %>%
  summarise(
    n=n(),
    mean=mean(rating),
    sd=sd(rating)
    ) %>%
  mutate(se=sd/sqrt(n-1))

avg_rating_top15 <- avg_rating %>% arrange(desc(n)) %>% slice(1:15)

3.2 Cocoa Percentage (%)

Filter dataset with cocoa percentages < 70%
Create avg_percent through grouping of data by company location, summarizing the data to get the frequency count, mean and standard deviation
Passing through the output using “%>%” and use of “mutate” to create a new variable standard error (SE = standard deviation / sqrt(n - 1))
Order the final dataset by top 15 company frequencies

proportion cocoa percent (% chocolate)

avg_percent <- chocodf %>%
  filter(chocodf$cocoa_percent >=0.7) %>%
  group_by(company_location) %>%
  summarise(
    n=n(),
    mean=mean(cocoa_percent),
    sd=sd(cocoa_percent)
    ) %>%
  mutate(se=sd/sqrt(n-1))

avg_percent_top15 <- avg_percent %>% arrange(desc(n)) %>% slice(1:15)

4.0 Creating the Visualisation

4.1 Average Rating by Top 15 Companies (According to Frequency)

ggplot(avg_rating_top15) +
  geom_errorbar(
    aes(x=reorder(company_location,-n,), 
        ymin=mean-1.98*se,
        ymax=mean+1.98*se), 
    width=0.2, 
    colour="black", 
    alpha=0.9, 
    size=0.5) +
  geom_point(aes
           (x=company_location, 
            y=mean), 
           stat="identity", 
           color="red",
           size = 1.5,
           alpha=1) +
  xlab("Company Location") +
  ylab("Average Rating") +
  ggtitle("Standard error of mean rating of top 15 companies (based on frequency)") + 
  scale_x_discrete(guide = guide_axis(n.dodge = 2))

4.2 Average Cocoa Percentage by Top 15 Companies (According to Frequency)

ggplot(avg_percent_top15) +
  geom_errorbar(
    aes(x=reorder(company_location,-n,), 
        ymin=mean-1.98*se,
        ymax=mean+1.98*se), 
    width=0.2, 
    colour="black", 
    alpha=0.9, 
    size=0.5) +
  geom_point(aes
           (x=company_location, 
            y=mean), 
           stat="identity", 
           color="red",
           size = 1.5,
           alpha=1) +
  xlab("Company Location") +
  ylab("Average Cocoa Percentage (%)") +
  ggtitle("Standard error of mean cocoa percentage of top 15 companies (based on frequency)") + 
  scale_x_discrete(guide = guide_axis(n.dodge = 2))

4.3 Combining the Two Graphs Using plotly and crosstalk() method

We attempt to create an interactive plot to directly compare the two plots to identify trends.

The code chunk below does a left join of the two datasets avg_rating_top15 and avg_percent_top15 to create single dataset for the creation of the visualisation. The merge() functiionality is used.

##combining the two datasets

forggplotly <- merge(x=avg_rating_top15, y = avg_percent_top15, by = "company_location", all.x =TRUE)

4.3.1 Challenges Faced

Overlapping x-axis labels which is manually augmented using “theme(axis.text.x = element_text(angle = 45, size = 10))”

d <- highlight_key(forggplotly)

#rating (x), percent (y)

p1<- ggplot(d) +
  geom_errorbar(
    aes(x=reorder(company_location,-n.x,), 
        ymin=mean.x-1.98*se.x,
        ymax=mean.x+1.98*se.x), 
    width=0.2, 
    colour="black", 
    alpha=0.9, 
    size=0.5) +
  geom_point(aes
           (x=company_location, 
            y=mean.x), 
           stat="identity", 
           color="red",
           size = 1.5,
           alpha=1) +
  xlab("Company Location") +
  ylab("Average Rating") +
  theme(axis.text.x = element_text(angle = 45, size = 10)) +
  ggtitle("Standard error of mean rating of top 15 companies (based on frequency)")  

p2 <-ggplot(d) +
  geom_errorbar(
    aes(x=reorder(company_location,-n.y,), 
        ymin=mean.y-1.98*se.y,
        ymax=mean.y+1.98*se.y), 
    width=0.2, 
    colour="black", 
    alpha=0.9, 
    size=0.5) +
  geom_point(aes
           (x=company_location, 
            y=mean.y), 
           stat="identity", 
           color="red",
           size = 1.5,
           alpha=1) +
  xlab("Company Location") +
  ylab("Average Cocoa Percentage (%)") +
  theme(axis.text.x = element_text(angle = 45, size = 10)) +
  ggtitle("Standard error of mean cocoa percentage of top 15 companies 
          (based on frequency)") 

gg1 <- ggplotly(p1)
gg2 <- ggplotly(p2)



crosstalk::bscols(gg1,
                  gg2,
                  widths = 12)

Take-Home Exercise 3